Abstract: This work presents a methodology to create probability maps for spatial continuous attributes based on indicator geostatistical approaches. The indicator kriging and the indication simulation approaches can be used to infer approximations of conditional cumulative distribution functions (cdf) for continuous attributes at different spatial locations of interest. The cdfs are conditioned to a set of spatial points containing continuous attribute values and sampled in a geographic region of interest. The conditional cdfs are then used to infer probability maps of exceeding, or being smaller than, a given threshold, or a predefined attribute, value. In this work it was used an elevation data set sampled in Florianópolis Island, the capital of Brazilian state Santa Catarina, as a case study to illustrate the methodology to create such probability maps.